Measuring Repeat and NearRepeat Burglary Effects

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Measuring Repeat and Near-Repeat Burglary Effects

• Martin B. Short, Maria R. DOrsogna,
• P. Jeffrey Brantingham, George E. Tita

Maria Pavlovskaia
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Repeat and Near-Repeat Victimization

• Criminals likely to revisit crime scene
• Likely to rob neighbors of previous victims

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Why?

• Knowledge of entry modes and security
• Easy access to site
• Abundance of material possessions
• Knowledge of neighbors daily routines

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Data analysis

• Measured the distribution of wait times between
  successive burglaries
• Rapidly decaying function
• Conclusion houses likely to be robbed again
  within a short period of time of a burglary
• Thus repeat victimization hypothesis is true?

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Random Event Hypothesis

• Burglaries occur at random with rate ?
• Poisson process
• Wait times exponentially distributed

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Testing the REH

• Two different counting methods
• Sliding window method
• Monitors each house for ?max days after burglary
• Count the number of burglaries occur in that time
• Fixed window method
• Classify houses by number of times robbed
• Look at the distribution of wait times in each
  class

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Sliding Window Method

• Sample contains D days of data
• Data split into N blocks with crime rates ?i
• Corresponding weights wi
• Predicted distribution

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Sliding Window Method

• Long Beach Data Set

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Fixed Window Method

• Sample contains D days of data
• Only focus on houses robbed twice
• Predicted distribution

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Fixed Window Method

• Long Beach Data Set

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REH disproved

• Robberies are correlated as hypothesized
• Data supports the exact-repeat hypothesis
• Burglarized houses likely to be struck again
• Data also supports near-repeat hypothesis